{"id":"W4386311025","doi":"10.2139/ssrn.4552752","title":"Rapid Identification of Salmonella Serovars Enteritidis and Typhimurium Using Whole Cell Matrix Assisted Laser Desorption Ionization – Time of Flight Mass Spectrometry (MALDI-TOF MS) Coupled with Multivariate Analysis and Artificial Intelligence","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Bacterial Identification and Susceptibility Testing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Salmonella enteritidis; Mass spectrometry; Chromatography; Salmonella; Matrix-assisted laser desorption/ionization; Matrix (chemical analysis); Chemistry; Analytical Chemistry (journal); Time-of-flight mass spectrometry; Identification (biology); Multivariate statistics; Ionization; Desorption; Biology; Computer science; Bacteria; Adsorption","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004007927,0.0006477886,0.0004833157,0.0008254416,0.0001909122,0.0006334078,0.0002501212,0.0005598064,0.000799811],"category_scores_gemma":[0.0007131857,0.0002761342,0.0003435827,0.0003958394,0.0002327257,0.0007017567,0.0005382931,0.0006190104,0.0008932716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001711477,"about_ca_system_score_gemma":0.0002675206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004466339,"about_ca_topic_score_gemma":0.0006630638,"domain_scores_codex":[0.9996406,0.00003593472,0.00002339207,0.00009231382,0.0001706058,0.00003712455],"domain_scores_gemma":[0.9997509,0.00007727523,0.00004039535,0.0000261134,0.00007881768,0.00002648848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001117634,0.00002567127,0.0005560085,0.00003008563,0.000007895925,0.00002886174,0.00001385091,0.0001017342,0.9895955,0.00006325195,0.00007930666,0.009386149],"study_design_scores_gemma":[0.00003574114,0.0005721825,0.02000211,0.000007689756,0.00004242762,0.0006892448,0.00009089069,0.01641624,0.9591992,0.000589489,0.002327654,0.00002702244],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.8705634,0.002211416,0.1213236,0.0003254371,0.0001529137,0.0001428928,0.002035556,0.001134604,0.002110233],"genre_scores_gemma":[0.783751,0.001666391,0.2055893,0.0001805413,0.00005406233,0.0001267778,0.004136581,0.0001394151,0.004355885],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.0008254416,"threshold_uncertainty_score":0.002675593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01728966705916707,"score_gpt":0.2668998754181003,"score_spread":0.2496102083589332,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}